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MLOps

Manage the entire ML lifecycle, including data preparation, model training, deployment, monitoring, and maintenance with MLOps.

What is MLOps?

In the world of data & analytics, there are multiple methodologies that focus on improving collaboration, communciation and integration between teams, DevOps, DataOps and MLOps.

Where DevOps focusses more on automation and integration of processes between software development and IT operation teams by shortening the systems development cycle, DataOps focusses on accelerating development and deployment of data analytics pipelines while ensuring data quality, reliability, and governance.

The term ‘MLOps’, or Machine Learning Operations, goes well beyond the overly simplistic idea of ‘automating tasks in machine learning’, a common misconception. Various definitions exist, but we believe this one encapsulates the concept thoroughly:

“MLOps is a discipline that combines principles from DevOps and data engineering to manage the entire ML lifecycle, including data preparation, model training, deployment, monitoring, and maintenance.”

Our approach

We’ve concluded three basic step before getting started with MLOps

  1. Define your organization’s Data Science Maturity level

  2. Gather MLOps requirements

  3. Choose your MLOps Maturity level

The suitable MLOps maturity level for your organization correlates with your overall data science maturity. Factors such as the number of implemented use cases and the size of your data science team play a critical role in this determination. These levels demonstrate that MLOps can be tailored to match each organization’s stage of growth. You can start simple and scale as needed.

  • Level 1: manual processes
  • Level 2: automated development
  • Level 3: automated development and deployment

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